Generally, an embedded camera includes an imaging sensor to convert optical signals into electrical signals. Most embedded cameras are equipped with an auto exposure control. The auto exposure control measures the average light intensity of the image scene being photographed, and determines an appropriate exposure value for photographic capture. The dynamic range of the embedded camera's imaging sensor is the ratio of the highest to the lowest light intensity that can be recorded by it. Image sensors have limited dynamic range. The dynamic range of a typical image sensor is often many orders of magnitude less than the dynamic range of natural scenes comprising regions of varying radiances. As a result of this, a single image can capture only a small fraction of the light intensities and colour vividness of the scene.
An image captured with high exposure time represents the darker areas of the scene well, whereas an image at low exposure time captures the brighter scene regions better. Computational High Dynamic Range (HDR) photography and exposure bracket image compositing techniques capture the images of the scene at different exposures and fuse the visual information from all of them into one single image.
Various types of conventional HDR techniques are known in the prior art, wherein most of them use a Red-Green-Blue (RGB) colour space technique for image processing. For example, U.S. Pat. No. 8,606,042 describes a system and method for blending of exposure-bracketed images using weight distribution functions. This system and method is presented for generating a new digital output image by blending a plurality of digital input images capturing the same scene at different levels of exposure. Each new pixel for the new digital output image is derived from a group of corresponding aligned pixels from the digital input images. In order to determine weight for each pixel, in each group of mutually-aligned source-image pixels, a weight distribution function is applied to values of an image characteristic for the pixels in the group of corresponding aligned pixels, and a net weight is subsequently assigned to each of the pixels in the group. Pixel values of pixels in each group of mutually-aligned source-image pixels are modified based on the net weights assigned to the pixels in order to obtain a new pixel value for a corresponding new pixel in the new digital output image. The claimed system inputs images in RGB colour space followed by conversion to HSV/HSI (Hue, Saturation, and Value/Intensity) colour space for processing the digital image to evaluate image characteristics such as pixel luminance, colour saturation, contrast, image intensity and sharpness. However, the use of RGB colour space in the claimed system requires to access to and processing of all three components, i.e. red, green and blue values for each pixel to provide saturation and contrast (sharpness indicator) for each digital image. This leads to huge computational overhead and memory access penalty, especially on embedded platforms. The YUV colour space could be considered a candidate for the operations just described; it does have the advantage of the intensity channel being naturally available, resulting in identical speed up with respect to alignment and sharpness measurement. However, the colour purity measure is not naturally available and its derivation would be convoluted and time intensive, thus ruling out usage of the YUV colour space.
In conventional systems, fusion of differently exposed images derives three weights such as saturation, contrast and well-exposedness to indicate the colour purity, sharpness and intensity balance and fuses images based on these weights. The weighted pixel addition is performed at different scales of the resolution pyramid. Pyramiding is the representation of a digital image at different scales to ensure visual homogeneity when pixels from differently illuminated images are used to composite into a single image, either directly or after quality weighting. The digital image pyramids are sent to the alignment process. Typically, the system in the alignment process thresholds and aligns the differently exposed images around their statistical medians to negate or nullify the effect of the exposure difference. Further, the alignment process calculates the shift or misalignment with respect to the each pixels of reference image, of all scales in the digital image of each exposure in the pyramid pattern. However, the process in the conventional system fails to exploit the redundancy existent in the pyramid pattern due to the misalignment of all pixels. This misalignment of all pixels is due to the global motion. This leads to increased computational time for the alignment process especially when operating at highest pyramid resolution in a hand held device with limited processing power.
Conventional system gauges the saturation, i.e. colour purity of an image by computing the standard deviation across the red, green and blue components. However, such calculation of saturation may not result in pleasing colours in the fused image. The fused image in any other colour space does not lend itself optimally to provide post-processing operations such as colour enrichment, sharpening and colour modification.
Hence, there is need for a system and method of generating a perceptibly improved HDR image much faster, and with lesser memory.